256,551 research outputs found
Learning Linear Temporal Properties
We present two novel algorithms for learning formulas in Linear Temporal
Logic (LTL) from examples. The first learning algorithm reduces the learning
task to a series of satisfiability problems in propositional Boolean logic and
produces a smallest LTL formula (in terms of the number of subformulas) that is
consistent with the given data. Our second learning algorithm, on the other
hand, combines the SAT-based learning algorithm with classical algorithms for
learning decision trees. The result is a learning algorithm that scales to
real-world scenarios with hundreds of examples, but can no longer guarantee to
produce minimal consistent LTL formulas. We compare both learning algorithms
and demonstrate their performance on a wide range of synthetic benchmarks.
Additionally, we illustrate their usefulness on the task of understanding
executions of a leader election protocol
Learning Markov Decision Processes for Model Checking
Constructing an accurate system model for formal model verification can be
both resource demanding and time-consuming. To alleviate this shortcoming,
algorithms have been proposed for automatically learning system models based on
observed system behaviors. In this paper we extend the algorithm on learning
probabilistic automata to reactive systems, where the observed system behavior
is in the form of alternating sequences of inputs and outputs. We propose an
algorithm for automatically learning a deterministic labeled Markov decision
process model from the observed behavior of a reactive system. The proposed
learning algorithm is adapted from algorithms for learning deterministic
probabilistic finite automata, and extended to include both probabilistic and
nondeterministic transitions. The algorithm is empirically analyzed and
evaluated by learning system models of slot machines. The evaluation is
performed by analyzing the probabilistic linear temporal logic properties of
the system as well as by analyzing the schedulers, in particular the optimal
schedulers, induced by the learned models.Comment: In Proceedings QFM 2012, arXiv:1212.345
An Adaptive Dictionary Learning Approach for Modeling Dynamical Textures
Video representation is an important and challenging task in the computer
vision community. In this paper, we assume that image frames of a moving scene
can be modeled as a Linear Dynamical System. We propose a sparse coding
framework, named adaptive video dictionary learning (AVDL), to model a video
adaptively. The developed framework is able to capture the dynamics of a moving
scene by exploring both sparse properties and the temporal correlations of
consecutive video frames. The proposed method is compared with state of the art
video processing methods on several benchmark data sequences, which exhibit
appearance changes and heavy occlusions
Learning deterministic probabilistic automata from a model checking perspective
Probabilistic automata models play an important role in the formal design and analysis of hard- and software systems. In this area of applications, one is often interested in formal model-checking procedures for verifying critical system properties. Since adequate system models are often difficult to design manually, we are interested in learning models from observed system behaviors. To this end we adopt techniques for learning finite probabilistic automata, notably the Alergia algorithm. In this paper we show how to extend the basic algorithm to also learn automata models for both reactive and timed systems. A key question of our investigation is to what extent one can expect a learned model to be a good approximation for the kind of probabilistic properties one wants to verify by model checking. We establish theoretical convergence properties for the learning algorithm as well as for probability estimates of system properties expressed in linear time temporal logic and linear continuous stochastic logic. We empirically compare the learning algorithm with statistical model checking and demonstrate the feasibility of the approach for practical system verification
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